multic.object: a multic object

In multic: Quantitative Linkage Analysis Tools using the Variance Components Approach

Description

Object of class "multic" returned from the function multic.

Arguments

fam.log.liks	the log likelihoods and lod scores for each family at each marker (including the null hypothesis). fam.log.liks is a 3-dimensional matrix. The first dimension is indexed by the family identifiers. The second dimension is indexed by the words "log.lik" and "lod.score". The third dimension is indexed by the word "null" and the names of the marker file names. To calculate the family log likelihoods, calc.fam.log.liks = TRUE must be passed to multic via the ... parameter or a multic.control object. If fam.log.liks are not calculated, then fam.log.liks is a character vector providing instructions how to calculate the values.
fixed.effects	the estimate, standard error, t value, and p value of the fixed effects for the traits and covariates for the null hypothesis and each marker. fixed.effects is a 3-dimensional matrix. The first dimension is indexed by the trait and covariate names. The second dimension is indexed by the words "Estmate", "Std.err", "t.value", and "p.value". The third dimension is indexed by the word "null" and the marker file names.
polygenic	the estimate, standard error, Wald score, Wald score P-value, heritabilty estimate, standard error of the heritabilty estimate, and heritably estimate P-value for the variance and covariance of the polygenic effect of the formula for the null hypothesis and each marker. polygenic is a 3-dimensional matrix. The first dimension is indexed by the letter "s" followed by a 1, 2, etc. for the variance of the first trait, second trait, and so on or 12, 13, 23, etc. for the covariance between the first and second traits, first and third traits, second and third traits, and so on. The second dimension is indexed by the words "Estimate", "Std.err", "Wald", "W.p.value", "h^2", "se.h^2", and "h.p.value". The third dimension is indexed by the word "null" and the marker file names.
major.gene1	the estimate, standard error, Wald score, Wald score P-value, heritabilty estimate, standard error of the heritabilty estimate, and heritably estimate P-value for the variance and covariance of the major gene effect of formula for the null hypothesis and each marker. major.gene1 is a 3-dimensional matrix. The first dimension is indexed by the letters "mg" followed by a 1, 2, etc. for the variance of the first trait, second trait, and so on or 12, 13, 23, etc. for the covariance between the first and second traits, first and third traits, second and third traits, and so on. The second dimension is indexed by the words "Estimate", "Std.err", "Wald", "W.p.value", "h^2", "se.h^2", and "h.p.value". The third dimension is indexed by the word "null" and the marker file names.
environmental	the estimate, standard error, Wald score, and Wald score P-value for the variance and covariance of the environmental effect of formula for the null hypothesis and each marker. environmental is a 3-dimensional matrix. The first dimension is indexed by the letter "e" followed by a 1, 2, etc. for the variance of the first trait, second trait, and so on or 12, 13, 23, etc. for the covariance between the first and second traits, first and third traits, second and third traits, and so on. The second dimension is indexed by the words "Estimate", "Std.err", "Wald", and "W.p.value". The third dimension is indexed by the word "null" and the marker file names.
sibling.sibling	the estimate, standard error, Wald score, and Wald score P-value for the variance and covariance of the sibling to sibling effect of formula for the null hypothesis and each marker. sibling.sibling is a 3-dimensional matrix. The first dimension is indexed by the letters "sib" followed by a 1, 2, etc. for the variance of the first trait, second trait, and so on or 12, 13, 23, etc. for the covariance between the first and second traits, first and third traits, second and third traits, and so on. The second dimension is indexed by the words "Estimate", "Std.err", "Wald", and "W.p.value". The third dimension is indexed by the word "null" and the marker file names. To receive valuable data, the 5th member of constraints in the multic.control object must be set to not "F" (fixed).
parent.parent	the estimate, standard error, Wald score, and Wald score P-value for the variance and covariance of the parent to parent effect of formula for the null hypothesis and each marker. parent.parent is a 3-dimensional matrix. The first dimension is indexed by the letter "p" followed by a 1, 2, etc. for the variance of the first trait, second trait, and so on or 12, 13, 23, etc. for the covariance between the first and second traits, first and third traits, second and third traits, and so on. The second dimension is indexed by the words "Estimate", "Std.err", "Wald", and "W.p.value". The third dimension is indexed by the word "null" and the marker file names. To receive valuable data, the 6th member of constraints in the multic.control object must be set to not "F" (fixed).
parent.offspring	the estimate, standard error, Wald score, and Wald score P-value for the variance and covariance of the parent to offspring effect of formula for the null hypothesis and each marker. parent.offspring is a 3-dimensional matrix. The first dimension is indexed by the letter "q" followed by a 1, 2, etc. for the variance of the first trait, second trait, and so on or 12, 13, 23, etc. for the covariance between the first and second traits, first and third traits, second and third traits, and so on. The second dimension is indexed by the words "Estimate", "Std.err", "Wald", and "W.p.value". The third dimension is indexed by the word "null" and the marker file names. To receive valuable data, the 7th member of constraints in the multic.control object must be set to not "F" (fixed).
log.liks	the log likelihood, centimorgan distance, log likelihood status, and lod score and P-value for the null hypothesis and each marker. log.liks is a data.frame. The row names are "null" and the markder file names. The column names are "log.likelihood", "distance", "log.lik.status", "lod.score", and "p.value". The log likelihood status represents whether the log likelihood converged before the maximum interations allowed or not and have the values of either "converg" or "non-converg".
var.fixed	the variance of the fixed effects of the traits and covariates for the null hypothesis and each marker. var.fixed is a 3-dimensional matrix. The first and second dimensions are indexed by the trait and covariate names. The third dimension is indexed by the word "null" and the marker file names.
var.random	the variance of the polygenic, major gene, and environmental effects for the null hypothesis and each marker. var.random is a 3-dimensional matrix. The first and second dimensions are indexed as described by the polygenic, major.gene1, and environmental components above. The third dimension is indexed by the word "null" and the marker file names.
var.sandwich	a more precise variance estimator after using a sandwich estimator approach. This is only calculated if the multic object represents a univariate model. var.sandwich is a 3-dimensional matrix. The first and second dimensions are indexed by "s1", "mg1", and "e1". The third dimension is indexed by the word "null" and the marker file names.
cors	the Pearson, Spearman, genetic, environmental, and phenotypic correlations. cors is a list made up of the components "pearson", "spearman", "genetic", "environment", and "phenotype". Both "pearson" and "spearman" are their respective correlations between the traits and covariates. They are 2-dimensional matrices indexed by the trait and covariate names. "genetic", "environment", and "phenotype" are the respective correlations between the polygenic and environmenal estimates. They are 2 dimensional matrices. The first dimension is indexed by the word "null" and the marker file names. The second dimension is indexed as described by the covariance portions of the polygenic and environmenal components above.
v.matrices	the variance-covariance matrix of the trait (y) that incorporates the polygenic, major gene, shared common environment, and error matrices. v.matrices is a 2-dimensional matrix. The first dimension is indexed by the family identifier (famid) values. The second dimension is indexed by the word "null" and the marker file names. Currently, there are no individual identifiers on each of the V matrices. If the V matrices are not calculated, then v.matrices is a character vector providing instructions how to calculate the values.
residuals	the observed values minus the fitted values of the trait (y) divided by the square root of the V matrix for each family. If the residuals are not calculated, then residuals is a character vector providing instructions how to calculate the values.
descriptives	the total individuals used, mean, standard deviation, minimum, maximum, kurtosis, and skewness for each trait and covariate.
counts	various counts of the total number of pedigrees, people, females, males, and so on. This is mostly for passing data for print and summary to display and is very likely to be not useful to the user community.
call	how multic was called. call is a call object.
R.sq	the proportion of variance due to the covariates.
metadata	a list of useful data like start.time, finish.time, call, epsilon, trait.count, iterations, null.initial.values, method, etc.

Generation

This class of objects is returned by the multic function to represent a fitted variance components model.

Methods

Objects of this class have methods for the functions polygene, print, plot, fitted, residuals, and summary

See Also

multic

multic documentation built on Jan. 15, 2017, 9:30 a.m.